Overview

Dataset statistics

Number of variables40
Number of observations9879
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory320.0 B

Variable types

Numeric29
Categorical11

Warnings

blueFirstBlood is highly correlated with redFirstBloodHigh correlation
blueKills is highly correlated with blueAssists and 7 other fieldsHigh correlation
blueDeaths is highly correlated with blueGoldDiff and 7 other fieldsHigh correlation
blueAssists is highly correlated with blueKills and 5 other fieldsHigh correlation
blueEliteMonsters is highly correlated with blueDragons and 1 other fieldsHigh correlation
blueDragons is highly correlated with blueEliteMonsters and 2 other fieldsHigh correlation
blueHeralds is highly correlated with blueEliteMonstersHigh correlation
blueTotalGold is highly correlated with blueKills and 9 other fieldsHigh correlation
blueAvgLevel is highly correlated with blueTotalGold and 8 other fieldsHigh correlation
blueTotalExperience is highly correlated with blueTotalGold and 8 other fieldsHigh correlation
blueTotalMinionsKilled is highly correlated with blueAvgLevel and 2 other fieldsHigh correlation
blueGoldDiff is highly correlated with blueKills and 17 other fieldsHigh correlation
blueExperienceDiff is highly correlated with blueKills and 14 other fieldsHigh correlation
blueCSPerMin is highly correlated with blueAvgLevel and 2 other fieldsHigh correlation
blueGoldPerMin is highly correlated with blueKills and 9 other fieldsHigh correlation
redFirstBlood is highly correlated with blueFirstBloodHigh correlation
redKills is highly correlated with blueDeaths and 7 other fieldsHigh correlation
redDeaths is highly correlated with blueKills and 7 other fieldsHigh correlation
redAssists is highly correlated with blueDeaths and 5 other fieldsHigh correlation
redEliteMonsters is highly correlated with blueDragons and 2 other fieldsHigh correlation
redDragons is highly correlated with blueDragons and 1 other fieldsHigh correlation
redHeralds is highly correlated with redEliteMonstersHigh correlation
redTotalGold is highly correlated with blueDeaths and 9 other fieldsHigh correlation
redAvgLevel is highly correlated with blueGoldDiff and 6 other fieldsHigh correlation
redTotalExperience is highly correlated with blueGoldDiff and 8 other fieldsHigh correlation
redTotalMinionsKilled is highly correlated with redTotalExperience and 1 other fieldsHigh correlation
redGoldDiff is highly correlated with blueKills and 17 other fieldsHigh correlation
redExperienceDiff is highly correlated with blueKills and 14 other fieldsHigh correlation
redCSPerMin is highly correlated with redTotalExperience and 1 other fieldsHigh correlation
redGoldPerMin is highly correlated with blueDeaths and 9 other fieldsHigh correlation
target is highly correlated with blueGoldDiff and 1 other fieldsHigh correlation
blueFirstBlood is highly correlated with redFirstBloodHigh correlation
blueKills is highly correlated with blueAssists and 7 other fieldsHigh correlation
blueDeaths is highly correlated with blueGoldDiff and 7 other fieldsHigh correlation
blueAssists is highly correlated with blueKills and 5 other fieldsHigh correlation
blueEliteMonsters is highly correlated with blueDragons and 1 other fieldsHigh correlation
blueDragons is highly correlated with blueEliteMonsters and 2 other fieldsHigh correlation
blueHeralds is highly correlated with blueEliteMonstersHigh correlation
blueTotalGold is highly correlated with blueKills and 9 other fieldsHigh correlation
blueAvgLevel is highly correlated with blueTotalGold and 6 other fieldsHigh correlation
blueTotalExperience is highly correlated with blueTotalGold and 8 other fieldsHigh correlation
blueTotalMinionsKilled is highly correlated with blueTotalExperience and 1 other fieldsHigh correlation
blueGoldDiff is highly correlated with blueKills and 17 other fieldsHigh correlation
blueExperienceDiff is highly correlated with blueKills and 14 other fieldsHigh correlation
blueCSPerMin is highly correlated with blueTotalExperience and 1 other fieldsHigh correlation
blueGoldPerMin is highly correlated with blueKills and 9 other fieldsHigh correlation
redFirstBlood is highly correlated with blueFirstBloodHigh correlation
redKills is highly correlated with blueDeaths and 7 other fieldsHigh correlation
redDeaths is highly correlated with blueKills and 7 other fieldsHigh correlation
redAssists is highly correlated with blueDeaths and 5 other fieldsHigh correlation
redEliteMonsters is highly correlated with blueDragons and 2 other fieldsHigh correlation
redDragons is highly correlated with blueDragons and 1 other fieldsHigh correlation
redHeralds is highly correlated with redEliteMonstersHigh correlation
redTotalGold is highly correlated with blueDeaths and 9 other fieldsHigh correlation
redAvgLevel is highly correlated with blueGoldDiff and 6 other fieldsHigh correlation
redTotalExperience is highly correlated with blueGoldDiff and 8 other fieldsHigh correlation
redTotalMinionsKilled is highly correlated with redTotalExperience and 1 other fieldsHigh correlation
redGoldDiff is highly correlated with blueKills and 17 other fieldsHigh correlation
redExperienceDiff is highly correlated with blueKills and 14 other fieldsHigh correlation
redCSPerMin is highly correlated with redTotalExperience and 1 other fieldsHigh correlation
redGoldPerMin is highly correlated with blueDeaths and 9 other fieldsHigh correlation
target is highly correlated with blueGoldDiff and 1 other fieldsHigh correlation
blueFirstBlood is highly correlated with redFirstBloodHigh correlation
blueKills is highly correlated with blueAssists and 3 other fieldsHigh correlation
blueDeaths is highly correlated with redKills and 3 other fieldsHigh correlation
blueAssists is highly correlated with blueKills and 3 other fieldsHigh correlation
blueEliteMonsters is highly correlated with blueDragons and 1 other fieldsHigh correlation
blueDragons is highly correlated with blueEliteMonsters and 2 other fieldsHigh correlation
blueHeralds is highly correlated with blueEliteMonstersHigh correlation
blueTotalGold is highly correlated with blueKills and 7 other fieldsHigh correlation
blueAvgLevel is highly correlated with blueTotalExperience and 2 other fieldsHigh correlation
blueTotalExperience is highly correlated with blueAvgLevel and 4 other fieldsHigh correlation
blueTotalMinionsKilled is highly correlated with blueCSPerMinHigh correlation
blueGoldDiff is highly correlated with blueTotalGold and 8 other fieldsHigh correlation
blueExperienceDiff is highly correlated with blueTotalGold and 10 other fieldsHigh correlation
blueCSPerMin is highly correlated with blueTotalMinionsKilledHigh correlation
blueGoldPerMin is highly correlated with blueKills and 7 other fieldsHigh correlation
redFirstBlood is highly correlated with blueFirstBloodHigh correlation
redKills is highly correlated with blueDeaths and 3 other fieldsHigh correlation
redDeaths is highly correlated with blueKills and 3 other fieldsHigh correlation
redAssists is highly correlated with blueDeaths and 3 other fieldsHigh correlation
redEliteMonsters is highly correlated with blueDragons and 2 other fieldsHigh correlation
redDragons is highly correlated with blueDragons and 1 other fieldsHigh correlation
redHeralds is highly correlated with redEliteMonstersHigh correlation
redTotalGold is highly correlated with blueDeaths and 7 other fieldsHigh correlation
redAvgLevel is highly correlated with blueExperienceDiff and 2 other fieldsHigh correlation
redTotalExperience is highly correlated with blueGoldDiff and 4 other fieldsHigh correlation
redTotalMinionsKilled is highly correlated with redCSPerMinHigh correlation
redGoldDiff is highly correlated with blueTotalGold and 8 other fieldsHigh correlation
redExperienceDiff is highly correlated with blueTotalGold and 10 other fieldsHigh correlation
redCSPerMin is highly correlated with redTotalMinionsKilledHigh correlation
redGoldPerMin is highly correlated with blueDeaths and 7 other fieldsHigh correlation
blueAvgLevel is highly correlated with blueExperienceDiff and 11 other fieldsHigh correlation
blueExperienceDiff is highly correlated with blueAvgLevel and 18 other fieldsHigh correlation
redFirstBlood is highly correlated with blueFirstBloodHigh correlation
redTowersDestroyed is highly correlated with redGoldDiffHigh correlation
blueGoldDiff is highly correlated with blueAvgLevel and 20 other fieldsHigh correlation
blueCSPerMin is highly correlated with blueAvgLevel and 5 other fieldsHigh correlation
redDeaths is highly correlated with blueExperienceDiff and 10 other fieldsHigh correlation
redTotalGold is highly correlated with blueExperienceDiff and 12 other fieldsHigh correlation
target is highly correlated with blueExperienceDiff and 6 other fieldsHigh correlation
redTotalExperience is highly correlated with blueExperienceDiff and 13 other fieldsHigh correlation
blueTotalJungleMinionsKilled is highly correlated with blueAvgLevel and 1 other fieldsHigh correlation
redGoldPerMin is highly correlated with blueExperienceDiff and 12 other fieldsHigh correlation
redGoldDiff is highly correlated with blueAvgLevel and 15 other fieldsHigh correlation
redAvgLevel is highly correlated with blueExperienceDiff and 14 other fieldsHigh correlation
blueTotalMinionsKilled is highly correlated with blueAvgLevel and 5 other fieldsHigh correlation
blueDeaths is highly correlated with blueAvgLevel and 13 other fieldsHigh correlation
blueKills is highly correlated with blueExperienceDiff and 10 other fieldsHigh correlation
redTotalJungleMinionsKilled is highly correlated with redTotalExperience and 1 other fieldsHigh correlation
redCSPerMin is highly correlated with blueExperienceDiff and 10 other fieldsHigh correlation
redAssists is highly correlated with blueGoldDiff and 4 other fieldsHigh correlation
blueFirstBlood is highly correlated with redFirstBloodHigh correlation
blueDragons is highly correlated with redDragons and 1 other fieldsHigh correlation
blueAssists is highly correlated with blueGoldDiff and 4 other fieldsHigh correlation
blueGoldPerMin is highly correlated with blueAvgLevel and 15 other fieldsHigh correlation
redEliteMonsters is highly correlated with redDragons and 1 other fieldsHigh correlation
blueTowersDestroyed is highly correlated with blueExperienceDiff and 9 other fieldsHigh correlation
redTotalMinionsKilled is highly correlated with blueExperienceDiff and 10 other fieldsHigh correlation
redDragons is highly correlated with blueDragons and 1 other fieldsHigh correlation
redExperienceDiff is highly correlated with blueAvgLevel and 20 other fieldsHigh correlation
redKills is highly correlated with blueAvgLevel and 13 other fieldsHigh correlation
blueEliteMonsters is highly correlated with blueDragons and 1 other fieldsHigh correlation
blueTotalGold is highly correlated with blueAvgLevel and 15 other fieldsHigh correlation
blueTotalExperience is highly correlated with blueAvgLevel and 15 other fieldsHigh correlation
redFirstBlood is highly correlated with blueFirstBloodHigh correlation
redDragons is highly correlated with blueDragons and 1 other fieldsHigh correlation
blueHeralds is highly correlated with blueEliteMonstersHigh correlation
blueFirstBlood is highly correlated with redFirstBloodHigh correlation
blueDragons is highly correlated with redDragons and 2 other fieldsHigh correlation
blueEliteMonsters is highly correlated with blueHeralds and 1 other fieldsHigh correlation
redEliteMonsters is highly correlated with redDragons and 2 other fieldsHigh correlation
redHeralds is highly correlated with redEliteMonstersHigh correlation
gameId has unique values Unique
blueWardsDestroyed has 745 (7.5%) zeros Zeros
blueAssists has 217 (2.2%) zeros Zeros
redWardsDestroyed has 785 (7.9%) zeros Zeros
redAssists has 235 (2.4%) zeros Zeros

Reproduction

Analysis started2021-12-29 01:41:31.686522
Analysis finished2021-12-29 01:43:03.138820
Duration1 minute and 31.45 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

UNIQUE

Distinct9879
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4500084045
Minimum4295358071
Maximum4527990640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:03.224566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4295358071
5-th percentile4448162119
Q14483301169
median4510920346
Q34521733208
95-th percentile4526401226
Maximum4527990640
Range232632569
Interquartile range (IQR)38432039.5

Descriptive statistics

Standard deviation27573278.49
Coefficient of variation (CV)0.006127280783
Kurtosis3.334606538
Mean4500084045
Median Absolute Deviation (MAD)13393261
Skewness-1.459122438
Sum4.445633028 × 1013
Variance7.602856867 × 1014
MonotonicityNot monotonic
2021-12-29T09:43:03.321763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44737863701
 
< 0.1%
44573764641
 
< 0.1%
45000360391
 
< 0.1%
45122491031
 
< 0.1%
44860750791
 
< 0.1%
44706709401
 
< 0.1%
44761893861
 
< 0.1%
45073333231
 
< 0.1%
45246225411
 
< 0.1%
45272757421
 
< 0.1%
Other values (9869)9869
99.9%
ValueCountFrequency (%)
42953580711
< 0.1%
42960047841
< 0.1%
42960366921
< 0.1%
42963545351
< 0.1%
42972090681
< 0.1%
42974224681
< 0.1%
42981148791
< 0.1%
42981813461
< 0.1%
42988745621
< 0.1%
43057267811
< 0.1%
ValueCountFrequency (%)
45279906401
< 0.1%
45279604591
< 0.1%
45279096971
< 0.1%
45279088581
< 0.1%
45278984861
< 0.1%
45278887981
< 0.1%
45278875421
< 0.1%
45278852401
< 0.1%
45278780581
< 0.1%
45278753171
< 0.1%

blueWardsPlaced
Real number (ℝ≥0)

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.28828829
Minimum5
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:03.416411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q114
median16
Q320
95-th percentile53
Maximum250
Range245
Interquartile range (IQR)6

Descriptive statistics

Standard deviation18.01917652
Coefficient of variation (CV)0.8084594152
Kurtosis23.43945163
Mean22.28828829
Median Absolute Deviation (MAD)2
Skewness4.136352605
Sum220186
Variance324.6907223
MonotonicityNot monotonic
2021-12-29T09:43:03.518548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161255
12.7%
151217
12.3%
17988
10.0%
14974
 
9.9%
18831
 
8.4%
13694
 
7.0%
19483
 
4.9%
12447
 
4.5%
20288
 
2.9%
11239
 
2.4%
Other values (137)2463
24.9%
ValueCountFrequency (%)
52
 
< 0.1%
71
 
< 0.1%
816
 
0.2%
939
 
0.4%
1096
 
1.0%
11239
 
2.4%
12447
 
4.5%
13694
7.0%
14974
9.9%
151217
12.3%
ValueCountFrequency (%)
2501
< 0.1%
2211
< 0.1%
2091
< 0.1%
2031
< 0.1%
1981
< 0.1%
1851
< 0.1%
1831
< 0.1%
1761
< 0.1%
1671
< 0.1%
1651
< 0.1%

blueWardsDestroyed
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.824881061
Minimum0
Maximum27
Zeros745
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:03.601147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum27
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.174998382
Coefficient of variation (CV)0.7699433482
Kurtosis17.19675844
Mean2.824881061
Median Absolute Deviation (MAD)1
Skewness2.845981594
Sum27907
Variance4.730617963
MonotonicityNot monotonic
2021-12-29T09:43:03.677465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
22357
23.9%
32116
21.4%
11790
18.1%
41413
14.3%
5746
 
7.6%
0745
 
7.5%
6345
 
3.5%
7163
 
1.6%
868
 
0.7%
922
 
0.2%
Other values (17)114
 
1.2%
ValueCountFrequency (%)
0745
 
7.5%
11790
18.1%
22357
23.9%
32116
21.4%
41413
14.3%
5746
 
7.6%
6345
 
3.5%
7163
 
1.6%
868
 
0.7%
922
 
0.2%
ValueCountFrequency (%)
271
 
< 0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
222
 
< 0.1%
212
 
< 0.1%
203
 
< 0.1%
199
0.1%
1811
0.1%
1710
0.1%

blueFirstBlood
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
1
4987 
0
4892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
14987
50.5%
04892
49.5%

Length

2021-12-29T09:43:03.816600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:03.863811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
14987
50.5%
04892
49.5%

Most occurring characters

ValueCountFrequency (%)
14987
50.5%
04892
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14987
50.5%
04892
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14987
50.5%
04892
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14987
50.5%
04892
49.5%

blueKills
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.183925499
Minimum0
Maximum22
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:03.914340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.011027975
Coefficient of variation (CV)0.4869120716
Kurtosis0.2637881975
Mean6.183925499
Median Absolute Deviation (MAD)2
Skewness0.5385175399
Sum61091
Variance9.066289468
MonotonicityNot monotonic
2021-12-29T09:43:03.983760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
61322
13.4%
51302
13.2%
41186
12.0%
71138
11.5%
8942
9.5%
3917
9.3%
9717
7.3%
2609
6.2%
10527
 
5.3%
11340
 
3.4%
Other values (11)879
8.9%
ValueCountFrequency (%)
063
 
0.6%
1313
 
3.2%
2609
6.2%
3917
9.3%
41186
12.0%
51302
13.2%
61322
13.4%
71138
11.5%
8942
9.5%
9717
7.3%
ValueCountFrequency (%)
221
 
< 0.1%
192
 
< 0.1%
184
 
< 0.1%
1713
 
0.1%
1630
 
0.3%
1538
 
0.4%
1464
 
0.6%
13147
1.5%
12204
2.1%
11340
3.4%

blueDeaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.137665756
Minimum0
Maximum22
Zeros72
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:04.056638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.933817709
Coefficient of variation (CV)0.4780021959
Kurtosis0.2140976082
Mean6.137665756
Median Absolute Deviation (MAD)2
Skewness0.5074928208
Sum60634
Variance8.607286351
MonotonicityNot monotonic
2021-12-29T09:43:04.128827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
51341
13.6%
61293
13.1%
41221
12.4%
71188
12.0%
8942
9.5%
3934
9.5%
9734
7.4%
2603
6.1%
10494
 
5.0%
11331
 
3.4%
Other values (11)798
8.1%
ValueCountFrequency (%)
072
 
0.7%
1270
 
2.7%
2603
6.1%
3934
9.5%
41221
12.4%
51341
13.6%
61293
13.1%
71188
12.0%
8942
9.5%
9734
7.4%
ValueCountFrequency (%)
221
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
178
 
0.1%
1620
 
0.2%
1532
 
0.3%
1466
 
0.7%
13114
 
1.2%
12211
2.1%
11331
3.4%

blueAssists
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.64510578
Minimum0
Maximum29
Zeros217
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:04.204743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q39
95-th percentile14
Maximum29
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0645199
Coefficient of variation (CV)0.6116561623
Kurtosis1.159114492
Mean6.64510578
Median Absolute Deviation (MAD)3
Skewness0.8902611921
Sum65647
Variance16.52032202
MonotonicityNot monotonic
2021-12-29T09:43:04.286962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
51068
10.8%
41010
10.2%
6935
9.5%
3926
9.4%
7880
8.9%
8843
8.5%
2731
 
7.4%
9648
 
6.6%
10541
 
5.5%
1468
 
4.7%
Other values (20)1829
18.5%
ValueCountFrequency (%)
0217
 
2.2%
1468
4.7%
2731
7.4%
3926
9.4%
41010
10.2%
51068
10.8%
6935
9.5%
7880
8.9%
8843
8.5%
9648
6.6%
ValueCountFrequency (%)
292
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
263
 
< 0.1%
257
0.1%
246
 
0.1%
237
0.1%
2212
0.1%
2111
0.1%
2015
0.2%

blueEliteMonsters
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
5156 
1
4013 
2
710 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
05156
52.2%
14013
40.6%
2710
 
7.2%

Length

2021-12-29T09:43:04.436477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:04.481170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
05156
52.2%
14013
40.6%
2710
 
7.2%

Most occurring characters

ValueCountFrequency (%)
05156
52.2%
14013
40.6%
2710
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05156
52.2%
14013
40.6%
2710
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05156
52.2%
14013
40.6%
2710
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05156
52.2%
14013
40.6%
2710
 
7.2%

blueDragons
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
6303 
1
3576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06303
63.8%
13576
36.2%

Length

2021-12-29T09:43:04.598837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:04.642330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
06303
63.8%
13576
36.2%

Most occurring characters

ValueCountFrequency (%)
06303
63.8%
13576
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06303
63.8%
13576
36.2%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06303
63.8%
13576
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06303
63.8%
13576
36.2%

blueHeralds
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
8022 
1
1857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
08022
81.2%
11857
 
18.8%

Length

2021-12-29T09:43:04.757621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:04.802525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
08022
81.2%
11857
 
18.8%

Most occurring characters

ValueCountFrequency (%)
08022
81.2%
11857
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08022
81.2%
11857
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08022
81.2%
11857
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08022
81.2%
11857
 
18.8%

blueTowersDestroyed
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
9415 
1
 
429
2
 
27
3
 
7
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09415
95.3%
1429
 
4.3%
227
 
0.3%
37
 
0.1%
41
 
< 0.1%

Length

2021-12-29T09:43:04.919700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:04.965993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
09415
95.3%
1429
 
4.3%
227
 
0.3%
37
 
0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09415
95.3%
1429
 
4.3%
227
 
0.3%
37
 
0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09415
95.3%
1429
 
4.3%
227
 
0.3%
37
 
0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09415
95.3%
1429
 
4.3%
227
 
0.3%
37
 
0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09415
95.3%
1429
 
4.3%
227
 
0.3%
37
 
0.1%
41
 
< 0.1%

blueTotalGold
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4739
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16503.45551
Minimum10730
Maximum23701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:05.029146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10730
5-th percentile14194
Q115415.5
median16398
Q317459
95-th percentile19190.5
Maximum23701
Range12971
Interquartile range (IQR)2043.5

Descriptive statistics

Standard deviation1535.446636
Coefficient of variation (CV)0.09303788744
Kurtosis0.479311511
Mean16503.45551
Median Absolute Deviation (MAD)1016
Skewness0.4682475245
Sum163037637
Variance2357596.373
MonotonicityNot monotonic
2021-12-29T09:43:05.124616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169569
 
0.1%
158859
 
0.1%
167499
 
0.1%
156498
 
0.1%
166888
 
0.1%
169408
 
0.1%
159678
 
0.1%
179518
 
0.1%
154207
 
0.1%
158717
 
0.1%
Other values (4729)9798
99.2%
ValueCountFrequency (%)
107301
< 0.1%
120021
< 0.1%
121781
< 0.1%
122921
< 0.1%
123001
< 0.1%
124031
< 0.1%
125191
< 0.1%
125981
< 0.1%
126221
< 0.1%
126821
< 0.1%
ValueCountFrequency (%)
237011
< 0.1%
234241
< 0.1%
233591
< 0.1%
233491
< 0.1%
233351
< 0.1%
232781
< 0.1%
232051
< 0.1%
228451
< 0.1%
227451
< 0.1%
226971
< 0.1%

blueAvgLevel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.916003644
Minimum4.6
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:05.204399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.4
Q16.8
median7
Q37.2
95-th percentile7.4
Maximum8
Range3.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3051458223
Coefficient of variation (CV)0.04412169773
Kurtosis1.116166722
Mean6.916003644
Median Absolute Deviation (MAD)0.2
Skewness-0.3385015794
Sum68323.2
Variance0.09311397286
MonotonicityNot monotonic
2021-12-29T09:43:05.272520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
72611
26.4%
6.82442
24.7%
7.21779
18.0%
6.61339
13.6%
7.4684
 
6.9%
6.4578
 
5.9%
6.2175
 
1.8%
7.6174
 
1.8%
643
 
0.4%
7.828
 
0.3%
Other values (7)26
 
0.3%
ValueCountFrequency (%)
4.61
 
< 0.1%
4.81
 
< 0.1%
5.22
 
< 0.1%
5.43
 
< 0.1%
5.64
 
< 0.1%
5.813
 
0.1%
643
 
0.4%
6.2175
 
1.8%
6.4578
5.9%
6.61339
13.6%
ValueCountFrequency (%)
82
 
< 0.1%
7.828
 
0.3%
7.6174
 
1.8%
7.4684
 
6.9%
7.21779
18.0%
72611
26.4%
6.82442
24.7%
6.61339
13.6%
6.4578
 
5.9%
6.2175
 
1.8%

blueTotalExperience
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4143
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17928.11013
Minimum10098
Maximum22224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:05.356521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10098
5-th percentile15934.8
Q117168
median17951
Q318724
95-th percentile19826.1
Maximum22224
Range12126
Interquartile range (IQR)1556

Descriptive statistics

Standard deviation1200.523764
Coefficient of variation (CV)0.0669632078
Kurtosis0.6803633556
Mean17928.11013
Median Absolute Deviation (MAD)779
Skewness-0.2485876264
Sum177111800
Variance1441257.309
MonotonicityNot monotonic
2021-12-29T09:43:05.464865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1853012
 
0.1%
1815910
 
0.1%
184599
 
0.1%
184959
 
0.1%
191169
 
0.1%
185979
 
0.1%
179089
 
0.1%
178579
 
0.1%
187509
 
0.1%
180739
 
0.1%
Other values (4133)9785
99.0%
ValueCountFrequency (%)
100981
< 0.1%
108261
< 0.1%
112861
< 0.1%
119211
< 0.1%
121111
< 0.1%
122121
< 0.1%
125561
< 0.1%
127981
< 0.1%
131191
< 0.1%
131661
< 0.1%
ValueCountFrequency (%)
222241
< 0.1%
221251
< 0.1%
218981
< 0.1%
218001
< 0.1%
217011
< 0.1%
216501
< 0.1%
216421
< 0.1%
216251
< 0.1%
215881
< 0.1%
215751
< 0.1%

blueTotalMinionsKilled
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.6995647
Minimum90
Maximum283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:05.569995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile180
Q1202
median218
Q3232
95-th percentile251
Maximum283
Range193
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.85843738
Coefficient of variation (CV)0.1008697798
Kurtosis0.1726223606
Mean216.6995647
Median Absolute Deviation (MAD)15
Skewness-0.2677707839
Sum2140775
Variance477.7912845
MonotonicityNot monotonic
2021-12-29T09:43:05.668400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
218193
 
2.0%
220192
 
1.9%
222190
 
1.9%
229186
 
1.9%
221185
 
1.9%
214183
 
1.9%
226183
 
1.9%
225175
 
1.8%
223175
 
1.8%
215175
 
1.8%
Other values (138)8042
81.4%
ValueCountFrequency (%)
901
 
< 0.1%
1201
 
< 0.1%
1231
 
< 0.1%
1301
 
< 0.1%
1311
 
< 0.1%
1361
 
< 0.1%
1371
 
< 0.1%
1382
< 0.1%
1401
 
< 0.1%
1413
< 0.1%
ValueCountFrequency (%)
2831
 
< 0.1%
2811
 
< 0.1%
2792
 
< 0.1%
2766
0.1%
2751
 
< 0.1%
2741
 
< 0.1%
2731
 
< 0.1%
2726
0.1%
2716
0.1%
2709
0.1%

blueTotalJungleMinionsKilled
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.50966697
Minimum0
Maximum92
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:05.773266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q144
median50
Q356
95-th percentile68
Maximum92
Range92
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.898282162
Coefficient of variation (CV)0.1959680741
Kurtosis0.3853278147
Mean50.50966697
Median Absolute Deviation (MAD)6
Skewness0.1169791562
Sum498985
Variance97.97598976
MonotonicityNot monotonic
2021-12-29T09:43:05.867848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48816
 
8.3%
52772
 
7.8%
44690
 
7.0%
56618
 
6.3%
60484
 
4.9%
40456
 
4.6%
51332
 
3.4%
47323
 
3.3%
64315
 
3.2%
55300
 
3.0%
Other values (64)4773
48.3%
ValueCountFrequency (%)
02
 
< 0.1%
43
< 0.1%
61
 
< 0.1%
162
 
< 0.1%
182
 
< 0.1%
193
< 0.1%
206
0.1%
212
 
< 0.1%
222
 
< 0.1%
236
0.1%
ValueCountFrequency (%)
921
 
< 0.1%
881
 
< 0.1%
852
 
< 0.1%
844
 
< 0.1%
836
 
0.1%
822
 
< 0.1%
815
 
0.1%
8019
0.2%
796
 
0.1%
782
 
< 0.1%

blueGoldDiff
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6047
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.41411074
Minimum-10830
Maximum11467
Zeros2
Zeros (%)< 0.1%
Negative4917
Negative (%)49.8%
Memory size77.3 KiB
2021-12-29T09:43:05.962685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-10830
5-th percentile-4033.2
Q1-1585.5
median14
Q31596
95-th percentile4074
Maximum11467
Range22297
Interquartile range (IQR)3181.5

Descriptive statistics

Standard deviation2453.349179
Coefficient of variation (CV)170.2046851
Kurtosis0.2994089
Mean14.41411074
Median Absolute Deviation (MAD)1592
Skewness0.03003750876
Sum142397
Variance6018922.196
MonotonicityNot monotonic
2021-12-29T09:43:06.055283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4288
 
0.1%
11677
 
0.1%
-18067
 
0.1%
-9786
 
0.1%
-19726
 
0.1%
-2596
 
0.1%
-2116
 
0.1%
-7586
 
0.1%
-3556
 
0.1%
-12086
 
0.1%
Other values (6037)9815
99.4%
ValueCountFrequency (%)
-108301
< 0.1%
-103291
< 0.1%
-93411
< 0.1%
-91521
< 0.1%
-84721
< 0.1%
-84611
< 0.1%
-79521
< 0.1%
-79111
< 0.1%
-78681
< 0.1%
-78661
< 0.1%
ValueCountFrequency (%)
114671
< 0.1%
89771
< 0.1%
88631
< 0.1%
87761
< 0.1%
86671
< 0.1%
86571
< 0.1%
85531
< 0.1%
85321
< 0.1%
84501
< 0.1%
83471
< 0.1%

blueExperienceDiff
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5356
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-33.6203057
Minimum-9333
Maximum8348
Zeros1
Zeros (%)< 0.1%
Negative5014
Negative (%)50.8%
Memory size77.3 KiB
2021-12-29T09:43:06.148286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-9333
5-th percentile-3206.1
Q1-1290.5
median-28
Q31212
95-th percentile3109.3
Maximum8348
Range17681
Interquartile range (IQR)2502.5

Descriptive statistics

Standard deviation1920.370438
Coefficient of variation (CV)-57.11936279
Kurtosis0.3648478761
Mean-33.6203057
Median Absolute Deviation (MAD)1252
Skewness0.02287603635
Sum-332135
Variance3687822.62
MonotonicityNot monotonic
2021-12-29T09:43:06.250784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
638
 
0.1%
4117
 
0.1%
-297
 
0.1%
-10257
 
0.1%
-14767
 
0.1%
-2987
 
0.1%
-2267
 
0.1%
-7296
 
0.1%
-1206
 
0.1%
-10646
 
0.1%
Other values (5346)9811
99.3%
ValueCountFrequency (%)
-93331
< 0.1%
-85311
< 0.1%
-82901
< 0.1%
-82421
< 0.1%
-73401
< 0.1%
-64881
< 0.1%
-64141
< 0.1%
-63651
< 0.1%
-63171
< 0.1%
-62101
< 0.1%
ValueCountFrequency (%)
83481
< 0.1%
82651
< 0.1%
76451
< 0.1%
76211
< 0.1%
76091
< 0.1%
67031
< 0.1%
65581
< 0.1%
65351
< 0.1%
64881
< 0.1%
64661
< 0.1%

blueCSPerMin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.66995647
Minimum9
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:06.354480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18
Q120.2
median21.8
Q323.2
95-th percentile25.1
Maximum28.3
Range19.3
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.185843738
Coefficient of variation (CV)0.1008697798
Kurtosis0.1726223606
Mean21.66995647
Median Absolute Deviation (MAD)1.5
Skewness-0.2677707839
Sum214077.5
Variance4.777912845
MonotonicityNot monotonic
2021-12-29T09:43:06.451839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.8193
 
2.0%
22192
 
1.9%
22.2190
 
1.9%
22.9186
 
1.9%
22.1185
 
1.9%
21.4183
 
1.9%
22.6183
 
1.9%
22.5175
 
1.8%
21.5175
 
1.8%
22.3175
 
1.8%
Other values (138)8042
81.4%
ValueCountFrequency (%)
91
 
< 0.1%
121
 
< 0.1%
12.31
 
< 0.1%
131
 
< 0.1%
13.11
 
< 0.1%
13.61
 
< 0.1%
13.71
 
< 0.1%
13.82
< 0.1%
141
 
< 0.1%
14.13
< 0.1%
ValueCountFrequency (%)
28.31
 
< 0.1%
28.11
 
< 0.1%
27.92
 
< 0.1%
27.66
0.1%
27.51
 
< 0.1%
27.41
 
< 0.1%
27.31
 
< 0.1%
27.26
0.1%
27.16
0.1%
279
0.1%

blueGoldPerMin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4739
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1650.345551
Minimum1073
Maximum2370.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:06.547503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1073
5-th percentile1419.4
Q11541.55
median1639.8
Q31745.9
95-th percentile1919.05
Maximum2370.1
Range1297.1
Interquartile range (IQR)204.35

Descriptive statistics

Standard deviation153.5446636
Coefficient of variation (CV)0.09303788744
Kurtosis0.479311511
Mean1650.345551
Median Absolute Deviation (MAD)101.6
Skewness0.4682475245
Sum16303763.7
Variance23575.96373
MonotonicityNot monotonic
2021-12-29T09:43:06.643357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1588.59
 
0.1%
1695.69
 
0.1%
1674.99
 
0.1%
1795.18
 
0.1%
16948
 
0.1%
1668.88
 
0.1%
1564.98
 
0.1%
1596.78
 
0.1%
1587.17
 
0.1%
1693.87
 
0.1%
Other values (4729)9798
99.2%
ValueCountFrequency (%)
10731
< 0.1%
1200.21
< 0.1%
1217.81
< 0.1%
1229.21
< 0.1%
12301
< 0.1%
1240.31
< 0.1%
1251.91
< 0.1%
1259.81
< 0.1%
1262.21
< 0.1%
1268.21
< 0.1%
ValueCountFrequency (%)
2370.11
< 0.1%
2342.41
< 0.1%
2335.91
< 0.1%
2334.91
< 0.1%
2333.51
< 0.1%
2327.81
< 0.1%
2320.51
< 0.1%
2284.51
< 0.1%
2274.51
< 0.1%
2269.71
< 0.1%

redWardsPlaced
Real number (ℝ≥0)

Distinct151
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.36795222
Minimum6
Maximum276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:06.734491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q114
median16
Q320
95-th percentile53
Maximum276
Range270
Interquartile range (IQR)6

Descriptive statistics

Standard deviation18.4574268
Coefficient of variation (CV)0.8251728461
Kurtosis30.47400826
Mean22.36795222
Median Absolute Deviation (MAD)2
Skewness4.560659971
Sum220973
Variance340.6766039
MonotonicityNot monotonic
2021-12-29T09:43:06.824558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151212
12.3%
161206
12.2%
171055
10.7%
141038
10.5%
18733
 
7.4%
13684
 
6.9%
19521
 
5.3%
12453
 
4.6%
20302
 
3.1%
11233
 
2.4%
Other values (141)2442
24.7%
ValueCountFrequency (%)
62
 
< 0.1%
76
 
0.1%
88
 
0.1%
940
 
0.4%
1094
 
1.0%
11233
 
2.4%
12453
 
4.6%
13684
6.9%
141038
10.5%
151212
12.3%
ValueCountFrequency (%)
2761
< 0.1%
2681
< 0.1%
2301
< 0.1%
2161
< 0.1%
2131
< 0.1%
2071
< 0.1%
2051
< 0.1%
2031
< 0.1%
1931
< 0.1%
1911
< 0.1%

redWardsDestroyed
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.723150116
Minimum0
Maximum24
Zeros785
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:06.905364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.138356115
Coefficient of variation (CV)0.7852509128
Kurtosis18.23703126
Mean2.723150116
Median Absolute Deviation (MAD)1
Skewness2.949099499
Sum26902
Variance4.572566873
MonotonicityNot monotonic
2021-12-29T09:43:06.980076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
22421
24.5%
32026
20.5%
11951
19.7%
41346
13.6%
0785
 
7.9%
5736
 
7.5%
6316
 
3.2%
7120
 
1.2%
842
 
0.4%
926
 
0.3%
Other values (15)110
 
1.1%
ValueCountFrequency (%)
0785
 
7.9%
11951
19.7%
22421
24.5%
32026
20.5%
41346
13.6%
5736
 
7.5%
6316
 
3.2%
7120
 
1.2%
842
 
0.4%
926
 
0.3%
ValueCountFrequency (%)
243
 
< 0.1%
231
 
< 0.1%
223
 
< 0.1%
213
 
< 0.1%
206
0.1%
196
0.1%
188
0.1%
178
0.1%
165
0.1%
159
0.1%

redFirstBlood
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
4987 
1
4892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
04987
50.5%
14892
49.5%

Length

2021-12-29T09:43:07.126126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:07.172133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
04987
50.5%
14892
49.5%

Most occurring characters

ValueCountFrequency (%)
04987
50.5%
14892
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04987
50.5%
14892
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04987
50.5%
14892
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04987
50.5%
14892
49.5%

redKills
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.137665756
Minimum0
Maximum22
Zeros72
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:07.224284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.933817709
Coefficient of variation (CV)0.4780021959
Kurtosis0.2140976082
Mean6.137665756
Median Absolute Deviation (MAD)2
Skewness0.5074928208
Sum60634
Variance8.607286351
MonotonicityNot monotonic
2021-12-29T09:43:07.296669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
51341
13.6%
61293
13.1%
41221
12.4%
71188
12.0%
8942
9.5%
3934
9.5%
9734
7.4%
2603
6.1%
10494
 
5.0%
11331
 
3.4%
Other values (11)798
8.1%
ValueCountFrequency (%)
072
 
0.7%
1270
 
2.7%
2603
6.1%
3934
9.5%
41221
12.4%
51341
13.6%
61293
13.1%
71188
12.0%
8942
9.5%
9734
7.4%
ValueCountFrequency (%)
221
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
178
 
0.1%
1620
 
0.2%
1532
 
0.3%
1466
 
0.7%
13114
 
1.2%
12211
2.1%
11331
3.4%

redDeaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.183925499
Minimum0
Maximum22
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:07.376833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.011027975
Coefficient of variation (CV)0.4869120716
Kurtosis0.2637881975
Mean6.183925499
Median Absolute Deviation (MAD)2
Skewness0.5385175399
Sum61091
Variance9.066289468
MonotonicityNot monotonic
2021-12-29T09:43:07.454707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
61322
13.4%
51302
13.2%
41186
12.0%
71138
11.5%
8942
9.5%
3917
9.3%
9717
7.3%
2609
6.2%
10527
 
5.3%
11340
 
3.4%
Other values (11)879
8.9%
ValueCountFrequency (%)
063
 
0.6%
1313
 
3.2%
2609
6.2%
3917
9.3%
41186
12.0%
51302
13.2%
61322
13.4%
71138
11.5%
8942
9.5%
9717
7.3%
ValueCountFrequency (%)
221
 
< 0.1%
192
 
< 0.1%
184
 
< 0.1%
1713
 
0.1%
1630
 
0.3%
1538
 
0.4%
1464
 
0.6%
13147
1.5%
12204
2.1%
11340
3.4%

redAssists
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.66211155
Minimum0
Maximum28
Zeros235
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:07.536194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q39
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.060612445
Coefficient of variation (CV)0.6095083241
Kurtosis0.7854096066
Mean6.66211155
Median Absolute Deviation (MAD)3
Skewness0.8233761136
Sum65815
Variance16.48857343
MonotonicityNot monotonic
2021-12-29T09:43:07.610430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
51043
10.6%
4993
10.1%
6981
9.9%
7916
9.3%
3911
9.2%
8779
7.9%
2707
 
7.2%
9666
 
6.7%
10563
 
5.7%
1472
 
4.8%
Other values (18)1848
18.7%
ValueCountFrequency (%)
0235
 
2.4%
1472
4.8%
2707
7.2%
3911
9.2%
4993
10.1%
51043
10.6%
6981
9.9%
7916
9.3%
8779
7.9%
9666
6.7%
ValueCountFrequency (%)
281
 
< 0.1%
261
 
< 0.1%
252
 
< 0.1%
245
 
0.1%
239
 
0.1%
229
 
0.1%
2113
 
0.1%
2025
0.3%
1933
0.3%
1846
0.5%

redEliteMonsters
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
4947 
1
4202 
2
730 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04947
50.1%
14202
42.5%
2730
 
7.4%

Length

2021-12-29T09:43:07.767332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:07.815291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
04947
50.1%
14202
42.5%
2730
 
7.4%

Most occurring characters

ValueCountFrequency (%)
04947
50.1%
14202
42.5%
2730
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04947
50.1%
14202
42.5%
2730
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04947
50.1%
14202
42.5%
2730
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04947
50.1%
14202
42.5%
2730
 
7.4%

redDragons
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
5798 
1
4081 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05798
58.7%
14081
41.3%

Length

2021-12-29T09:43:07.945973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:07.994509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
05798
58.7%
14081
41.3%

Most occurring characters

ValueCountFrequency (%)
05798
58.7%
14081
41.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05798
58.7%
14081
41.3%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05798
58.7%
14081
41.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05798
58.7%
14081
41.3%

redHeralds
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
8298 
1
1581 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08298
84.0%
11581
 
16.0%

Length

2021-12-29T09:43:08.124266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:08.169149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
08298
84.0%
11581
 
16.0%

Most occurring characters

ValueCountFrequency (%)
08298
84.0%
11581
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08298
84.0%
11581
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08298
84.0%
11581
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08298
84.0%
11581
 
16.0%

redTowersDestroyed
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
9483 
1
 
367
2
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09483
96.0%
1367
 
3.7%
229
 
0.3%

Length

2021-12-29T09:43:08.291347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:08.340356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
09483
96.0%
1367
 
3.7%
229
 
0.3%

Most occurring characters

ValueCountFrequency (%)
09483
96.0%
1367
 
3.7%
229
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09483
96.0%
1367
 
3.7%
229
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09483
96.0%
1367
 
3.7%
229
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09483
96.0%
1367
 
3.7%
229
 
0.3%

redTotalGold
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4732
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16489.0414
Minimum11212
Maximum22732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:08.404609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum11212
5-th percentile14238.8
Q115427.5
median16378
Q317418.5
95-th percentile19137
Maximum22732
Range11520
Interquartile range (IQR)1991

Descriptive statistics

Standard deviation1490.888406
Coefficient of variation (CV)0.09041692415
Kurtosis0.2190001548
Mean16489.0414
Median Absolute Deviation (MAD)989
Skewness0.4107431599
Sum162895240
Variance2222748.238
MonotonicityNot monotonic
2021-12-29T09:43:08.502167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160749
 
0.1%
165538
 
0.1%
158818
 
0.1%
161548
 
0.1%
160388
 
0.1%
163798
 
0.1%
165618
 
0.1%
174048
 
0.1%
172717
 
0.1%
159677
 
0.1%
Other values (4722)9800
99.2%
ValueCountFrequency (%)
112121
< 0.1%
113571
< 0.1%
115021
< 0.1%
119571
< 0.1%
122751
< 0.1%
123381
< 0.1%
126261
< 0.1%
126511
< 0.1%
127241
< 0.1%
127251
< 0.1%
ValueCountFrequency (%)
227321
< 0.1%
226811
< 0.1%
226141
< 0.1%
224021
< 0.1%
223551
< 0.1%
222831
< 0.1%
222501
< 0.1%
221101
< 0.1%
220881
< 0.1%
220731
< 0.1%

redAvgLevel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.925316328
Minimum4.8
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:08.588265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile6.4
Q16.8
median7
Q37.2
95-th percentile7.4
Maximum8.2
Range3.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3053114187
Coefficient of variation (CV)0.04408627769
Kurtosis1.236949941
Mean6.925316328
Median Absolute Deviation (MAD)0.2
Skewness-0.3981092714
Sum68415.2
Variance0.09321506241
MonotonicityNot monotonic
2021-12-29T09:43:08.662373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
72672
27.0%
6.82392
24.2%
7.21838
18.6%
6.61275
12.9%
7.4713
 
7.2%
6.4540
 
5.5%
7.6196
 
2.0%
6.2150
 
1.5%
652
 
0.5%
5.818
 
0.2%
Other values (8)33
 
0.3%
ValueCountFrequency (%)
4.82
 
< 0.1%
51
 
< 0.1%
5.21
 
< 0.1%
5.43
 
< 0.1%
5.65
 
0.1%
5.818
 
0.2%
652
 
0.5%
6.2150
 
1.5%
6.4540
5.5%
6.61275
12.9%
ValueCountFrequency (%)
8.21
 
< 0.1%
83
 
< 0.1%
7.817
 
0.2%
7.6196
 
2.0%
7.4713
 
7.2%
7.21838
18.6%
72672
27.0%
6.82392
24.2%
6.61275
12.9%
6.4540
 
5.5%

redTotalExperience
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4113
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17961.73044
Minimum10465
Maximum22269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:08.753084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10465
5-th percentile15962
Q117209.5
median17974
Q318764.5
95-th percentile19879
Maximum22269
Range11804
Interquartile range (IQR)1555

Descriptive statistics

Standard deviation1198.583912
Coefficient of variation (CV)0.0667298686
Kurtosis0.8169216007
Mean17961.73044
Median Absolute Deviation (MAD)776
Skewness-0.2815310515
Sum177443935
Variance1436603.394
MonotonicityNot monotonic
2021-12-29T09:43:08.847238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1784210
 
0.1%
175059
 
0.1%
176809
 
0.1%
176089
 
0.1%
183089
 
0.1%
176969
 
0.1%
178549
 
0.1%
185349
 
0.1%
180379
 
0.1%
172129
 
0.1%
Other values (4103)9788
99.1%
ValueCountFrequency (%)
104651
< 0.1%
106101
< 0.1%
113511
< 0.1%
114431
< 0.1%
119991
< 0.1%
123971
< 0.1%
128461
< 0.1%
129831
< 0.1%
131051
< 0.1%
132641
< 0.1%
ValueCountFrequency (%)
222691
< 0.1%
222581
< 0.1%
219671
< 0.1%
218181
< 0.1%
217741
< 0.1%
217281
< 0.1%
217221
< 0.1%
216441
< 0.1%
216411
< 0.1%
216391
< 0.1%

redTotalMinionsKilled
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct153
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.3492256
Minimum107
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:08.936272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile180
Q1203
median218
Q3233
95-th percentile252
Maximum289
Range182
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.91166803
Coefficient of variation (CV)0.1008131865
Kurtosis0.2267048464
Mean217.3492256
Median Absolute Deviation (MAD)15
Skewness-0.2893107742
Sum2147193
Variance480.1211957
MonotonicityNot monotonic
2021-12-29T09:43:09.031011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215198
 
2.0%
218192
 
1.9%
220191
 
1.9%
225188
 
1.9%
221184
 
1.9%
214179
 
1.8%
226179
 
1.8%
216174
 
1.8%
210174
 
1.8%
211174
 
1.8%
Other values (143)8046
81.4%
ValueCountFrequency (%)
1071
< 0.1%
1171
< 0.1%
1231
< 0.1%
1292
< 0.1%
1322
< 0.1%
1331
< 0.1%
1341
< 0.1%
1351
< 0.1%
1361
< 0.1%
1371
< 0.1%
ValueCountFrequency (%)
2892
 
< 0.1%
2821
 
< 0.1%
2801
 
< 0.1%
2791
 
< 0.1%
2781
 
< 0.1%
2771
 
< 0.1%
2764
< 0.1%
2752
 
< 0.1%
2742
 
< 0.1%
2736
0.1%

redTotalJungleMinionsKilled
Real number (ℝ≥0)

HIGH CORRELATION

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.31308837
Minimum4
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:09.131020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile36
Q144
median51
Q357
95-th percentile68
Maximum92
Range88
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.02788488
Coefficient of variation (CV)0.1954254791
Kurtosis0.4156250078
Mean51.31308837
Median Absolute Deviation (MAD)7
Skewness0.2312291686
Sum506922
Variance100.5584752
MonotonicityNot monotonic
2021-12-29T09:43:10.039237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52894
 
9.0%
48850
 
8.6%
56719
 
7.3%
44686
 
6.9%
60549
 
5.6%
40475
 
4.8%
64317
 
3.2%
51310
 
3.1%
47296
 
3.0%
55265
 
2.7%
Other values (65)4518
45.7%
ValueCountFrequency (%)
41
 
< 0.1%
83
 
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
161
 
< 0.1%
171
 
< 0.1%
203
 
< 0.1%
223
 
< 0.1%
232
 
< 0.1%
2415
0.2%
ValueCountFrequency (%)
923
 
< 0.1%
911
 
< 0.1%
891
 
< 0.1%
884
 
< 0.1%
851
 
< 0.1%
8410
 
0.1%
833
 
< 0.1%
823
 
< 0.1%
815
 
0.1%
8037
0.4%

redGoldDiff
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6047
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-14.41411074
Minimum-11467
Maximum10830
Zeros2
Zeros (%)< 0.1%
Negative4960
Negative (%)50.2%
Memory size77.3 KiB
2021-12-29T09:43:10.127533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-11467
5-th percentile-4074
Q1-1596
median-14
Q31585.5
95-th percentile4033.2
Maximum10830
Range22297
Interquartile range (IQR)3181.5

Descriptive statistics

Standard deviation2453.349179
Coefficient of variation (CV)-170.2046851
Kurtosis0.2994089
Mean-14.41411074
Median Absolute Deviation (MAD)1592
Skewness-0.03003750876
Sum-142397
Variance6018922.196
MonotonicityNot monotonic
2021-12-29T09:43:10.220357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4288
 
0.1%
-11677
 
0.1%
18067
 
0.1%
-3246
 
0.1%
9786
 
0.1%
17616
 
0.1%
7586
 
0.1%
-10606
 
0.1%
2596
 
0.1%
-12596
 
0.1%
Other values (6037)9815
99.4%
ValueCountFrequency (%)
-114671
< 0.1%
-89771
< 0.1%
-88631
< 0.1%
-87761
< 0.1%
-86671
< 0.1%
-86571
< 0.1%
-85531
< 0.1%
-85321
< 0.1%
-84501
< 0.1%
-83471
< 0.1%
ValueCountFrequency (%)
108301
< 0.1%
103291
< 0.1%
93411
< 0.1%
91521
< 0.1%
84721
< 0.1%
84611
< 0.1%
79521
< 0.1%
79111
< 0.1%
78681
< 0.1%
78661
< 0.1%

redExperienceDiff
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5356
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.6203057
Minimum-8348
Maximum9333
Zeros1
Zeros (%)< 0.1%
Negative4864
Negative (%)49.2%
Memory size77.3 KiB
2021-12-29T09:43:10.310942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-8348
5-th percentile-3109.3
Q1-1212
median28
Q31290.5
95-th percentile3206.1
Maximum9333
Range17681
Interquartile range (IQR)2502.5

Descriptive statistics

Standard deviation1920.370438
Coefficient of variation (CV)57.11936279
Kurtosis0.3648478761
Mean33.6203057
Median Absolute Deviation (MAD)1252
Skewness-0.02287603635
Sum332135
Variance3687822.62
MonotonicityNot monotonic
2021-12-29T09:43:10.404872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-638
 
0.1%
10257
 
0.1%
297
 
0.1%
2267
 
0.1%
2987
 
0.1%
-4117
 
0.1%
14767
 
0.1%
-5576
 
0.1%
816
 
0.1%
-11056
 
0.1%
Other values (5346)9811
99.3%
ValueCountFrequency (%)
-83481
< 0.1%
-82651
< 0.1%
-76451
< 0.1%
-76211
< 0.1%
-76091
< 0.1%
-67031
< 0.1%
-65581
< 0.1%
-65351
< 0.1%
-64881
< 0.1%
-64661
< 0.1%
ValueCountFrequency (%)
93331
< 0.1%
85311
< 0.1%
82901
< 0.1%
82421
< 0.1%
73401
< 0.1%
64881
< 0.1%
64141
< 0.1%
63651
< 0.1%
63171
< 0.1%
62101
< 0.1%

redCSPerMin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct153
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.73492256
Minimum10.7
Maximum28.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:10.501036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10.7
5-th percentile18
Q120.3
median21.8
Q323.3
95-th percentile25.2
Maximum28.9
Range18.2
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.191166803
Coefficient of variation (CV)0.1008131865
Kurtosis0.2267048464
Mean21.73492256
Median Absolute Deviation (MAD)1.5
Skewness-0.2893107742
Sum214719.3
Variance4.801211957
MonotonicityNot monotonic
2021-12-29T09:43:10.593662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.5198
 
2.0%
21.8192
 
1.9%
22191
 
1.9%
22.5188
 
1.9%
22.1184
 
1.9%
21.4179
 
1.8%
22.6179
 
1.8%
21.6174
 
1.8%
21174
 
1.8%
21.1174
 
1.8%
Other values (143)8046
81.4%
ValueCountFrequency (%)
10.71
< 0.1%
11.71
< 0.1%
12.31
< 0.1%
12.92
< 0.1%
13.22
< 0.1%
13.31
< 0.1%
13.41
< 0.1%
13.51
< 0.1%
13.61
< 0.1%
13.71
< 0.1%
ValueCountFrequency (%)
28.92
 
< 0.1%
28.21
 
< 0.1%
281
 
< 0.1%
27.91
 
< 0.1%
27.81
 
< 0.1%
27.71
 
< 0.1%
27.64
< 0.1%
27.52
 
< 0.1%
27.42
 
< 0.1%
27.36
0.1%

redGoldPerMin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4732
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1648.90414
Minimum1121.2
Maximum2273.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2021-12-29T09:43:10.687410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1121.2
5-th percentile1423.88
Q11542.75
median1637.8
Q31741.85
95-th percentile1913.7
Maximum2273.2
Range1152
Interquartile range (IQR)199.1

Descriptive statistics

Standard deviation149.0888406
Coefficient of variation (CV)0.09041692415
Kurtosis0.2190001548
Mean1648.90414
Median Absolute Deviation (MAD)98.9
Skewness0.4107431599
Sum16289524
Variance22227.48238
MonotonicityNot monotonic
2021-12-29T09:43:10.780181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1607.49
 
0.1%
1603.88
 
0.1%
1656.18
 
0.1%
1655.38
 
0.1%
1740.48
 
0.1%
1637.98
 
0.1%
1588.18
 
0.1%
1615.48
 
0.1%
1581.37
 
0.1%
1661.57
 
0.1%
Other values (4722)9800
99.2%
ValueCountFrequency (%)
1121.21
< 0.1%
1135.71
< 0.1%
1150.21
< 0.1%
1195.71
< 0.1%
1227.51
< 0.1%
1233.81
< 0.1%
1262.61
< 0.1%
1265.11
< 0.1%
1272.41
< 0.1%
1272.51
< 0.1%
ValueCountFrequency (%)
2273.21
< 0.1%
2268.11
< 0.1%
2261.41
< 0.1%
2240.21
< 0.1%
2235.51
< 0.1%
2228.31
< 0.1%
22251
< 0.1%
22111
< 0.1%
2208.81
< 0.1%
2207.31
< 0.1%

target
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
4949 
1
4930 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04949
50.1%
14930
49.9%

Length

2021-12-29T09:43:10.940434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T09:43:10.985106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
04949
50.1%
14930
49.9%

Most occurring characters

ValueCountFrequency (%)
04949
50.1%
14930
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04949
50.1%
14930
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04949
50.1%
14930
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04949
50.1%
14930
49.9%

Interactions

2021-12-29T09:41:38.234719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:38.372004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:38.524809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:38.642536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:38.762077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:38.869981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:38.989799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.135376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.259801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.391410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.516046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.633175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.764105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:39.885132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.010162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.141050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.261845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.366677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.472443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.581445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.697334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.810891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:41:40.938555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-12-29T09:43:00.118814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.209125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.297890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.391610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.471333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.550884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.642693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.733783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.820892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.906770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:00.999512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.079140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.161060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.242007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.324621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.411409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.496789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.581222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.658592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.740931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.828043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-29T09:43:01.916565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-12-29T09:43:11.096770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-29T09:43:11.396555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-29T09:43:11.695670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-29T09:43:11.987490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-12-29T09:43:12.236882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-12-29T09:43:02.211511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-29T09:43:02.941225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

gameIdblueWardsPlacedblueWardsDestroyedblueFirstBloodblueKillsblueDeathsblueAssistsblueEliteMonstersblueDragonsblueHeraldsblueTowersDestroyedblueTotalGoldblueAvgLevelblueTotalExperienceblueTotalMinionsKilledblueTotalJungleMinionsKilledblueGoldDiffblueExperienceDiffblueCSPerMinblueGoldPerMinredWardsPlacedredWardsDestroyedredFirstBloodredKillsredDeathsredAssistsredEliteMonstersredDragonsredHeraldsredTowersDestroyedredTotalGoldredAvgLevelredTotalExperienceredTotalMinionsKilledredTotalJungleMinionsKilledredGoldDiffredExperienceDiffredCSPerMinredGoldPerMintarget
04519157822282196110000172106.61703919536643-819.51721.015606980000165676.81704719755-643819.71656.70
1452337194912105550000147126.61626517443-2908-117317.41471.212115522111176206.817438240522908117324.01762.00
24521474530150071141100161136.41622118646-1172-103318.61611.31531117140000172856.817254203281172103320.31728.50
3452438406743104551010151577.01795420155-1321-720.11515.7152154100000164787.017961235471321723.51647.80
4443603377175406660000164007.01854321057-100423021.01640.017216671100174047.018313225671004-23022.51740.40
5447536570918005361100158997.0181612254269810122.51589.936513520000152017.01806022159-698-10122.11520.11
6449301063218317671100168746.816967225532411156322.51687.457106790000144636.41540416435-2411-156316.41446.31
74496759358162051330000153056.41613820948-2615-80020.91530.51501135111100179206.61693815754261580015.71792.00
8444304803016307780000164017.21852718961-1979-77118.91640.115217752110183807.21929824053197977124.01838.00
9450943334613114551100150576.81680522039-1548-157422.01505.716205440000166056.818379247431548157424.71660.51

Last rows

gameIdblueWardsPlacedblueWardsDestroyedblueFirstBloodblueKillsblueDeathsblueAssistsblueEliteMonstersblueDragonsblueHeraldsblueTowersDestroyedblueTotalGoldblueAvgLevelblueTotalExperienceblueTotalMinionsKilledblueTotalJungleMinionsKilledblueGoldDiffblueExperienceDiffblueCSPerMinblueGoldPerMinredWardsPlacedredWardsDestroyedredFirstBloodredKillsredDeathsredAssistsredEliteMonstersredDragonsredHeraldsredTowersDestroyedredTotalGoldredAvgLevelredTotalExperienceredTotalMinionsKilledredTotalJungleMinionsKilledredGoldDiffredExperienceDiffredCSPerMinredGoldPerMintarget
986945278753171210912121100161987.01824916533-2121-103816.51619.8133112971010183197.419287187682121103818.71831.90
9870452781142546215320000169237.219758222721974171222.21692.311003551100149496.81804620264-1974-171220.21494.91
9871452771578112204552110151316.81821621461-72734321.41513.117415420000158586.81787324848727-34324.81585.80
9872452765039812017790000171557.01800223136756123.11715.560307781100163997.01800121658-756-121.61639.91
987345278780581821126130000185737.219391207462639236420.71857.3166061260000159346.61702719738-2639-236419.71593.41
9874452787328617217451100177657.218967211692519246921.11776.546304770000152466.81649822934-2519-246922.91524.61
9875452779746654006481100162387.2192552334878288823.31623.8122114630000154567.01836720656-782-88820.61545.61
9876452771371623106750000159037.01803221045-2416-187721.01590.3140176111100183197.419909261602416187726.11831.90
9877452762831314412331100144596.61722922448-839-108522.41445.966403210000152987.21831424740839108524.71529.80
9878452377293518016650000162667.01732120744927-5820.71626.69206641100153396.81737920146-9275820.11533.91